Use CPU scheduling
Cgroups with CPU scheduling helps you effectively manage mixed workloads.
MapReduce jobs only
If you primarily run MapReduce jobs on your cluster, enabling CPU scheduling does not change performance much. The dominant resource for MapReduce is memory, so the DRF scheduler continues to balance MapReduce jobs in a manner similar to the default resource calculator. In the case of a single resource, the DRF reduces to max-min fairness for that resource.
An example of a mixed workload is a cluster that runs both MapReduce and Storm on YARN. MapReduce is not CPU-constrained, but Storm on YARN is; its containers require more CPU than memory. As you add Storm jobs along with MapReduce jobs, the DRF scheduler tries to balance memory and CPU resources, but you might see some performance degradation in as a result. As you add more CPU-intensive Storm jobs, individual jobs start to take longer to run as the cluster CPU resources are consumed.
To solve this problem, you can use cgroups along with CPU scheduling. Using cgroups provides isolation for CPU-intensive processes such as Storm on YARN, thereby enabling you to predictably plan and constrain the CPU-intensive Storm containers.
You can also use partitions in conjunction with CPU scheduling and cgroups to restrict Storm on YARN jobs to a subset of cluster nodes.